Today’s highly competitive market has forced food and beverage manufacturers to look at all
alternatives to reduce waste and control
fill variation. Whereas overfill gives away
product, underfill can result in litigation,
fines or even worse — brand damage.

In any packaging process where a
product is being filled into individual
packages using a target or nominal
weight label, there is a risk of improper
filling. Underfilled packages violate the
Fair Packaging and Labeling Act (FPLA)
which requires manufacturers to disclose
net contents of a package. The Federal
Trade Commission (FTC) may pay a visit,
announced or sometimes unannounced,
to conduct random sampling and ensure
that manufacturers are properly filling
packaging. The enforcement of FPLA
is designed to investigate and stop
consumer deception, such as slack
fill. Underfill can result in temporary
discontinuation of a production line or
facility, causing loss of productivity,
rework or yield loss. It can also result
in consumer complaints, harm to brand
equity and diminished customer loyalty.

To avoid this risk, some manufacturers
think it is safer to overfill, but this is just
as bad. Overfilling results in unnecessary
product “giveaway,” reduced revenue and
lower margins. Consider a product with a
one-pound nominally labeled weight per
package, which holds a product worth
$1.00 per pound, and is processed at a
rate of 50,000 packages per week, 50
weeks per year. The savings generated by
a simple one percent reduction in overfill
could save 25,000 pounds annually, or
$25,000 per year.

Minimizing fill variation not only
reduces the amount of product that is
given away, it also lowers material costs
in packaging, storage and transportation.
So how is it done?

Many companies have tried to tackle theproblem of fill variation, but have faced thecommon challenge of insufficient historicaldata from the manufacturing lines. Withoutthis data, it can be difficult to uncover theroot causes of fill variation and effectivelycraft solutions. These six-steps, using LeanSix Sigma and statistical process capabilityanalysis, have been proven to reduce fillvariation and result in precision packagingfill, significant cost savings and reduced risk.

Step 1: Analyze Current DataAnalyzing current data from themanufacturing filling line is the first stepto determining the amount of underfill oroverfill that is taking place. Continuous datais required for fill analysis using statisticalprocess control and process capabilitymethods. This type of data is informationthat can be measured on a continuumor scale. The data is normally capturedin the operating system governing the fillprocess. Typically, within that fill processthere is a target or nominal weight. Thereare also tolerance limits: UCL, whichrepresents the upper control limit, and LCL,which represents the lower control limit.This information is generally displayedgraphically on a control chart (see Figure 1).

A control chart is a line graph that
displays a continuous picture of what
is happening in the production process
over time. It is an important tool for
statistical process control. The UCL and
LCL on a control chart indicate whether
the observed variation in the process is
within tolerance and therefore acceptable,
or whether the variation is caused by an
abnormal event that must be investigated.

Step 2: Understand Legalitiesand Company Policies

Understanding legalities and company
policies for the filling operation
is crucial when tackling
underfilling and overfilling.
Most company policies are
centered around the FTC
requirements for filling and
labeling a particular product.
Most packaged food goods
are pre-labeled with a nominal
weight at the processor, and
the regulatory requirements

133 (NIST HB133). Two aspects of FTC
packaging requirements are relevant to
overfill: the Maximum Allowable Variation
(MAV) and the Average Error (AE).